Robust Power System State Estimation With Minimum Error Entropy Unscented Kalman Filter

Dang, Lujuan; Chen, Badong; Wang, Shiyuan; Ma, Wentao; Ren, Pengju

doi:10.1109/tim.2020.2999757

Cited by 68 publications

(37 citation statements)

References 45 publications

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“…In most state estimation algorithms, the measurement observation model is highly nonlinear which can be solved by the possible use of Kalman filter for nonlinear systems [11]. The Kalman filtering techniques have been improved significantly over the past few years with the variants such as extended Kalman filter, unscented Kalman filter and robust minimum variance unscented Kalman filter [12].…”

Section: Cemsmentioning

confidence: 99%

“…A Kalman filter contains feedback loops and can be used for time series prediction with historical data [11]. The non-linear equations in kalman are not linearized as with other gradients methods [12].…”

Section: Kalman Filter As An Estimatormentioning

confidence: 99%

“…The non-linear equations in kalman are not linearized as with other gradients methods [12]. Instead, a statistical distribution of the state is represented through sigma points which provides estimates of the actual state and the posterior covariance matrix [11][12][13]. The test results in [1] were completed using DSE only and was limited to IEEE-14 and IEEE-30 Bus without the use of Kalman filter.…”

Section: Kalman Filter As An Estimatormentioning

confidence: 99%

See 2 more Smart Citations

State Estimation within IED Based Smart Grid Using Kalman Estimates

2021

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State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation.

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Section: Cemsmentioning

confidence: 99%

Section: Kalman Filter As An Estimatormentioning

confidence: 99%

Section: Kalman Filter As An Estimatormentioning

confidence: 99%

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State Estimation within IED Based Smart Grid Using Kalman Estimates

2021

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“…The latest works on anomaly detection use robust FASEs. 16,17,[38][39][40][41][42] However, this method still cannot detect the sudden load change if the change of states is small in comparison to the change of NI of the measurements. In addition, both the innovation analysis method and the robust FASE have not considered the detection of the topology change after fault, and the identification of the anomalies.…”

Section: Introductionmentioning

confidence: 99%

An improved algorithm for cubature Kalman filter based forecasting‐aided state estimation and anomaly detection

Jin

Chakrabarti

et al. 2021

Int Trans Electr Energ Syst

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This article proposes a new algorithm for forecasting-aided state estimation based on the cubature Kalman filter (CKF) and new methods for detecting and identifying data anomalies. In this article, through extensive simulations, the CKF was compared to four different types of forecasting-aided state estimators (FASEs) including extended Kalman filter (EKF), iterated EKF, second-order Kalman filter and unscented Kalman filter under normal operation and bad data conditions. Identifying the challenge that the estimation accuracy of the existing CKF-based estimator is significantly lower than that of the other FASEs in the cases of sudden load change, and sudden topology change caused by faults an attempt to improve the CKF accuracy has been undertaken. The existing detection methods cannot accurately detect and distinguish those anomalies, and they cannot identify the anomaly location. This article proposes an improved algorithm for CKF-based FASE that overcomes the drawbacks of the existing CKF-based FASEs using a novel anomaly detection algorithm. The simulation results show that the new anomaly detection method is superior to the two existing anomaly detection algorithms. The simulations are performed in the above-mentioned four cases in IEEE 14 and 118 bus test systems in MATLAB.

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“…The EKF algorithm linearizes the nonlinear system through the Taylor series expansion but ignores the higher order terms (Sun et al, 2017(Sun et al, , 2018, which cannot guarantee the high estimation accuracy. As for the UKF algorithm, the unscented transform (UT) technique is applied to approximate the probability distribution of the state variable after nonlinear transformation Dang et al, 2020). The UKF algorithm produces better estimation performance than the EKF algorithm, and it is more suitable to realize the online application of estimation.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations

Bai

Zheng

et al. 2021

Front. Energy Res.

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In this study, the forecasting-aided state estimation (FASE) problem for the active distribution system (ADS) with distributed generations (DGs) is investigated, considering the constraint of data transmission. First of all, the system model of the ADS with DGs is established, which expands the scope of the ADS state estimation from the power network to the DGs. Moreover, in order to improve the efficiency of data transmission under the limited communication bandwidth, a component-based event-triggered mechanism is employed to schedule the data transmission from the measurement terminals to the estimator. It can efficiently reduce the amount of data transmission while guaranteeing the performance of system state estimation. Second, an event-triggered unscented Kalman filter (ET-UKF) algorithm is proposed to conduct the state estimation of the ADS with mixed measurements. To this end, the unscented transform (UT) technique is employed to approximate the probability distribution of the state variable after nonlinear transformation, which can reach more than second order, and then, an upper bound of the filtering error covariance is derived and subsequently minimized at each iteration. The gain of the desired filter is obtained recursively by following a certain set of recursions. Finally, the effectiveness of the proposed method is demonstrated by using the IEEE-34 distribution test system.

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Robust Power System State Estimation With Minimum Error Entropy Unscented Kalman Filter

Cited by 68 publications

References 45 publications

State Estimation within IED Based Smart Grid Using Kalman Estimates

State Estimation within IED Based Smart Grid Using Kalman Estimates

An improved algorithm for cubature Kalman filter based forecasting‐aided state estimation and anomaly detection

Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations

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